Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions.
Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions.
This course gives you the tools to measure brand and customer assets, understand regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns. You'll leave the course with a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources.
For more information on marketing analytics, you may visit; http://dmanalytics.org. You can also follow my posts in Twitter, @rajkumarvenk, and on linkedin; www.linkedin.com/in/rajkumar-venkatesan-14970a3.

KP

I have reached week 4 of this class, and so far, this class has helped me a lot. It's not an intensive class, but for a beginner like me, this provides me with an overall grasp of the field.

GJ

Jul 17, 2018

Filled StarFilled StarFilled StarFilled StarFilled Star

Professor Raj is very nice. He explains it way that it's very easy to comprehend and not too overwhelming in terminologies. It is very useful for my marketing career\n\nGood luck Prof Raj

À partir de la leçon

Regression Basics

Ever wonder how variables influence consumer behavior in the real world--like how weather and a price promotion affect ice cream consumption? In this module, we will take a look at regression and how it's used to understand that relationship. We will discuss how to set up regressions and interpret outputs, explore confounding effects and biases, and distinguish between economic and statistical significance. We'll finish the week with a series of interviews with real marketing professionals who share their experiences and knowledge about how they use analytics on the job.

Enseigné par

Rajkumar Venkatesan

Ronald Trzcinski Professor of Business Administration

Transcription

A concept that's really important in marketing and that also has connections to regression is something called elasticity. Here we are going to look at price elasticity. What it means is, it is the percent change in sales for a percent change in price. So price elasticity is primarily change in sales over change in price multiplied by price over sales. And it is important to have this value over here, price over sales. That is different than just the coefficient. What we saw in regression was this set here, change in sales to change in price is your coefficient. Now if you take the coefficient and multiply that by average price over average sales, you would get price elasticity. Now, why are we so hung up on price elasticity? Why do we need elasticity? It is because elasticity has no units. It's unitless, which means every year you can measure elasticity, and track this elasticity over time so you can compare improvements or declines in the effectiveness of your market. So that's why it's a really useful concept to know. And we can see how it connects to regression through this value, the coefficient. But is there a way to modify, tweak the regression a little bit to just use the coefficient directly and it will be equal to elasticity? Let's see. So we're going to take the example of Belvedere Vodka. So far we have looked at made up numbers and you can say, hey, you're talking about made up numbers, I work with real people with real data give me some real examples. So here we go. What we have here is data from Belvedere Vodka over seven years in the US. So the data is from 2001 to 2007. We have sales of 9 liter cases of Belvedere Vodka and this is thousands. And what we are doing here is taking from this column to this column here, we're taking the log, which is the logarithmic transformation of sales, and we are going to look at logarithms and what they are in a short while. But for now, stay with me to understand that logarithm is a transformation that we make on the data. Now, we take a log transformation, so sales is 410, log of sales, is 6. This is price of 9 liter cases of Belvedere Vodka, $215. And log of that price is 5.3, and we have advertising how much advertising was done for Belvedere Vodka, and this is the log of that advertising value. And advertising is also in dollar. Thousands. So this is real data. So far, we have looked at made up data and you could be thinking, wait a minute you are showing me all this with made up data, but I am dealing with real people with real consumers in the real world. And does this regression apply in there. So here we have it, this is real data about Belvedere Vodka sales in the US and this is real data about the prices the managers at Belvedere Vodka set and how much they advertise in each of these seven years. So we're going to take all of this data and see the relationship between price and sales of Belvedere Vodka, and apply it into a regression model and come up with values that will then give us a relationship between price and sales. So let's see what we got here. So here is the output. Of the regression of log of price of Belvedere vodka on log of sales of Belvedere vodka. So let's see what the regression output gives us. So the R-squared is about 45%, this is how the data looks like. In the x axis, we have price and in the y axis, we have sales. And to be specific, we have log of price and log of sales. That's what we're plugging into the regression function. These green dots are the seven years of data and the black line is your regression equation. Just like we saw in the example. Now you know we then need to look at the coefficients and the p value. So the intercept is 12.68. So, if price was 0, that would be awesome. Free vodka, we all like it. If price was 0, then case sales, log of case sales of Belvedere Vodka is 12.6, the P-value is less than 0.05, which means, what does it mean? Think about it. Which means, if fee value is low, high confidence in the regression, right? So that's a good thing. Now next thing is Ln(Price). Coefficient is -1.25, P-value is less than 0.1, which is okay. It is still lower than the threshold of 10%. It's not great but it's good. It will do for now. But what do we have here, this is the coefficient, this the slope, this is the change in ln sale for change in ln price and that's the coefficient right here. Now here's the kicker, when you use ln of price as x and ln of sales as y the coefficient is the same as price elasticity. So now, by doing the log transformations on the x and y, and using that in the regression, you can actually just do the regression. Pick up the coefficient, and that gives you the elasticity, isn't that cool? Now we are going to see, very shortly, why that is the case. Why does doing what is called a log-log model give you elasticity?